Question 1

What is answer engine optimization (AEO)?

Accepted Answer

Answer engine optimization (AEO) is the practice of making a business legible to large-language-model answer engines like ChatGPT, Claude, Perplexity, and Google AI Overviews, so that when a buyer asks one of them for a recommendation in a category, the business is named in the answer. AEO is adjacent to SEO but optimizes for a different surface: a single synthesized answer, not a ranked list of blue links. The two disciplines share foundations (clean site, consistent listings, real reviews) but AEO weights plain, direct answers to real buyer questions much more heavily than keyword density or backlink volume.

Question 2

How do I get my business cited by ChatGPT?

Accepted Answer

There is no paid path. ChatGPT and other answer engines decide which businesses to recommend by reading sources they already trust: your Google Business Profile, your website, your directory listings, your reviews, and third-party mentions in publications and forums. To get cited, make sure robots.txt allows GPTBot, publish a well-formed llms.txt pointing the model at your three or four strongest pages, keep your business-information consistent across every directory, respond to every recent review by name, and publish one pillar page that defines the thing you want to be known for. Mention volume compounds over weeks, not days.

Question 3

What's the difference between AEO and SEO?

Accepted Answer

SEO ranks a page in a list of blue links on a search results page. AEO places a business inside a single synthesized answer that an AI engine reads out to the buyer. SEO rewards keyword relevance, link authority, and technical signals that help a crawler judge a page. AEO rewards source trustworthiness, factual consistency across the open web, and prose a model can lift verbatim. The same foundations help both. AEO adds LLM crawler access (robots.txt for GPTBot and ClaudeBot), llms.txt, structured FAQ content, and measurement of actual AI mention rate across model and query.

Question 4

Why does my business not appear when I search for it on ChatGPT?

Accepted Answer

Usually one of two reasons. Either the engines cannot verify the business (listings are inconsistent across directories, GBP categories are wrong, robots.txt is blocking GPTBot, there is no llms.txt pointing to anchor pages), or the market evidence is thin (few recent reviews, no third-party mentions, no pillar content the model can quote). A digital presence audit distinguishes the two: it tells you whether you're invisible to the engine or whether the engine can see you but has nothing worth citing.

Question 5

How do I show up when someone asks an AI for a plumber (or roofer, or dentist) near me?

Accepted Answer

Three moves in order of leverage. First, make sure your Google Business Profile is complete and categorized correctly, because every local answer engine draws from GBP data. Second, get your name, address, phone, hours, and categories matched across the top directories (Yelp, Facebook, BBB, Yellow Pages, Foursquare, Apple Maps, Bing Places, Angi, Thumbtack, Houzz, and the rest). Third, respond to every review you have, especially the recent ones, by name and with specifics. Then publish one pillar page about the service in the geography. These four steps move most businesses from unmentioned to mentioned within one to two measurement cycles.

Question 6

What is llms.txt and does my business need one?

Accepted Answer

llms.txt is a short Markdown file at the root of your domain (like robots.txt) that tells language-model crawlers which of your pages are the citation-worthy ones. The proposed spec is: one H1 with the business name, a blurb paragraph, and section headings for Services, Learn, Compare, FAQ, each with bulleted links to the canonical URL and a one-line summary. Yes, a local business should have one. It is cheap to create, it does not require developer time, and it is one of the fastest-acting signals a model picks up on when deciding what to quote about a business.

Question 7

Can I pay to be recommended by an AI like ChatGPT or Claude?

Accepted Answer

No. Anyone offering paid placement inside AI answers is selling something that does not exist. The major LLMs (OpenAI, Anthropic, Google, Perplexity) do not accept payment to be named in answers. They decide which businesses to recommend based on what they can verify from the open web: directory data, review profiles, structured data, and press mentions. A legitimate AEO engagement optimizes those signals. It does not buy placement.

Question 8

How long does AEO take to move the needle?

Accepted Answer

The first signals you control (robots.txt, llms.txt, GBP cleanup, schema) move within one to two weekly measurement cycles. The second-side signals (reviews, citations, press mentions, actual LLM mention rate across a query battery) typically take 60 to 120 days to show measurable lift, because the engines refresh on their own cadence and third-party sources crawl on theirs. If a vendor promises AEO lift in two weeks, they are either confusing you with first-side cleanup or they are selling theatre.

Question 9

How is AEO performance measured?

Accepted Answer

AEO performance is measured by running the same buyer queries against the major LLMs on a cadence (weekly works for most businesses) and recording three things per query: did the business appear in the answer, at what position (first-named, second-named, mentioned-in-passing), and what framing did the model use (positive, neutral, negative, misdescribed). The weekly scan feeds a trend over 60 to 120 days. 11 of the 18 axes in NoCode Labs' Digital Presence Score include a direct LLM mention-rate axis, so the AEO signal rolls up into the Earned score and the composite.

Question 10

What does an AEO audit include?

Accepted Answer

An AEO audit covers the full stack a model touches: robots.txt and llms.txt configuration, structured data (JSON-LD Organization, LocalBusiness, Service, FAQPage), GBP completeness and category accuracy, directory consistency across 16 directories, review profile depth and response quality, pillar and FAQ coverage, and a baseline sweep of major LLMs on a buyer-query battery to measure current mention rate. The NoCode Labs $500 Digital Presence Audit checks 18 axes end-to-end and returns a prioritized fix list with every change approved before anything deploys.

Answer engine optimization: 10 questions buyers ask.